An Inversion Algorithm for the Dynamic Modulus of Concrete Pavement Structures Based on a Convolutional Neural Network

被引:3
|
作者
Chen, Gongfa [1 ]
Chen, Xuedi [1 ]
Yang, Linqing [2 ,3 ]
Han, Zejun [2 ]
Bassir, David [4 ,5 ]
机构
[1] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China
[2] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China
[3] Guangzhou Inst Sci & Technol, Sch Architectural Engn, Guangzhou 510540, Peoples R China
[4] Univ Paris Saclay, Ctr Borelli, ENS, F-91190 Gif sur yvette, France
[5] UTBM, IRAMAT, CNRS, UMR 7065, Rue Leupe, F-90010 Belfort, France
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 02期
关键词
pavement structure; dynamic modulus; convolution neural network; inversion algorithm; falling weight deflectometer; EFFICIENT PARAMETER-IDENTIFICATION; SPECTRAL ELEMENT TECHNIQUE; GREENS-FUNCTIONS; LAYERED MEDIA; SIMULATION; TESTS; MODEL;
D O I
10.3390/app13021192
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Based on the spectral element method (SEM) and a convolutional neural network (CNN), an inversion algorithm for the dynamic modulus of concrete pavement structures is proposed in this paper. In order to evaluate the service performance of pavement structures more systematically and accurately via the existing testing techniques using a falling weight deflectometer (FWD), it is necessary to obtain accurate dynamic modulus parameters of the structures. In this work, an inversion algorithm for predicting the dynamic modulus is established by using a CNN which is trained with the dynamic response samples of a multi-layered concrete pavement structure obtained through SEM. The gradient descent method is used to adjust the weight parameters in the network layer by layer in reverse. As a result, the accuracy of the CNN can be improved via iterative training. With the proposed algorithm, more accurate results of the dynamic modulus of pavement structures are obtained. The accuracy and numerical stability of the proposed algorithm are verified by several numerical examples. The dynamic modulus and thickness of concrete pavement structure layers can be accurately predicted by the CNN trained with a certain number of training samples based on the displacement curve of the deflection basin from the falling weight deflectometer. The proposed method can provide a reliable testing tool for the FWD technique of pavement structures.
引用
收藏
页数:18
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